Exploring the echoes reverberating through time left by the technology of yesterday as we embrace the technology of tomorrow.
OpenAI surprise-launches GPT-5.1 with a tone-picker. Anthropic reportedly commits $200B to Google Cloud. Moonshot's Kimi K2 Thinking, a trillion-parameter open-weight model that beats GPT-5. Microsoft Ignite warming up. My take on the week.
The legal framework for treating accumulated organizational knowledge as a real asset is older than the AI conversation and incomplete in ways the AI conversation makes more visible. Worth pulling on the thread.
When something is broken you get three attempts to fix it before you must stop and re-plan. Why 'just one more try' is the failure mode, and why the rule matters most when an agent is the one trying.
The NVIDIA device plugin, node taints and tolerations, the multi-tenancy reality of one GPU per pod vs MIG vs vGPU. The scheduling primitives that work at homelab scale on a single 1080 Ti and the ones that change shape entirely at fleet scale.
DSPy lets you optimize prompts the way you'd optimize a model, programmatically, against an objective, with structured evaluation. The default DSPy workflow sends a lot of prompts to a hosted optimizer. The local-first version is doable, faster than expected, and worth the setup.
OpenAI-AWS $38B makes multi-cloud official. Moonshot's Kimi K2 Thinking is the first open-source model to beat GPT-5 on agentic benchmarks. Apple reportedly pays Google $1B/yr for Siri. Ironwood TPUs ship. The concentration story cracked.
The default for AI in 2025 is hosted. The hosted default makes sense for some workloads and is structurally wrong for others. Worth being explicit about which workloads belong on-prem and why the inertia is hard to break.
The first article of my personal coding constitution: every change starts with a tracked issue, not a commit. I had to write this rule down for myself before I'd live by it. Then I had to write it down again for the agents I work with.
Every AI team I've talked to has the same problem: private information moves through their tools, and nobody on the team owns it. The gap is real, it's getting wider, and something will force the issue. Here's the breakdown, and who should own it.
GitHub Universe ships Agent HQ. Big-tech earnings push 2025 AI capex past a quarter trillion. Amazon cuts 14k jobs the same week. OpenAI completes its PBC restructuring. Cursor ships its own model. The week that made the AI capex-vs-jobs trade explicit.
Latency, token throughput, cost-per-request, queue depth, drift. Prometheus for the metrics, Loki for the prompts and responses with PII discipline, Tempo for the tracing across agent calls. The dashboard JSON itself as a Decisions as Code surface, projected per-environment.
The keynote pitch for agent orchestration is engineering-shaped, pipelines, queues, message buses. The framing that holds up better in production is workforce-shaped, roles, teams, supervisors, performance review. Worth being explicit about why.
